Improved YOLOv5 Based Deep Learning System for Jellyfish Detection-Reference-Cited by-同舟云学术

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Improved YOLOv5 Based Deep Learning System for Jellyfish Detection

Published:2024 Issue: Volume:12 Page:87838-87849
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Pham Thi-Ngot¹,Nguyen Viet-Hoan²,Kwon Ki-Ryong²^ORCID,Kim Jae-Hwan³^ORCID,Huh Jun-Ho⁴^ORCID

Affiliation:

1. Department of Data Informatics, National Korea Maritime and Ocean University, Busan, Republic of Korea

2. Department of Artificial Intelligence Convergence, Pukyong National University, Busan, Republic of Korea

3. Department of Data Science, National Korea Maritime and Ocean University, Busan, Republic of Korea

4. Interdisciplinary Major of Ocean Renewable Energy Engineering, National Korea Maritime and Ocean University, Busan, Republic of Korea

Funder

KMOU Graduate School Innovation - Fellowship Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10538227.pdf?arnumber=10538227

Reference57 articles.

1. The preliminary studies on the dynamics of macro-jellyfish resources and their relationship with fisheries in the East China Sea and yellow sea;Yan;Mar. Fisheries,2004

2. The morphology and structure of jellyfish (Cyanea nozakii kishinouye);Dong;Fish Sci.,2005

3. Object Detection in 20 Years: A Survey

4. LLaMA: Open and efficient foundation language models;Touvron;arXiv:2302.13971,2023

5. Llama 2: Open foundation and fine-tuned chat models;Touvron;arXiv:2307.09288,2023

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